8872 • The Journal of Neuroscience, August 24, 2016 • 36(34):8872–8881

Behavioral/Cognitive Neural Basis of Acquired Amusia and Its Recovery after Stroke

X Aleksi J. Sihvonen,1,2 XPablo Ripolle´s,3,4 Vera Leo,2 XAntoni Rodríguez-Fornells,3,4 Seppo Soinila,5 and X Teppo Sa¨rka¨mo¨2 1Faculty of Medicine, University of Turku, 20520 Turku, Finland, 2Cognitive Brain Research Unit, Institute of Behavioural Sciences, University of Helsinki, 00014 Helsinki, Finland, 3Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute, L’Hospitalet de Llobregat, 08907 Barcelona, Spain, 4Department of Basic Psychology, University of Barcelona, 08035 Barcelona, Spain, and 5Division of Clinical Neurosciences, Turku University Hospital and Department of Neurology, University of Turku, 20521, Turku, Finland

Although acquired amusia is a relatively common disorder after stroke, its precise neuroanatomical basis is still unknown. To evaluate which brain regions form the neural substrate for acquired amusia and its recovery, we performed a voxel-based lesion-symptom mapping (VLSM) and morphometry (VBM) study with 77 human stroke subjects. Structural MRIs were acquired at acute and 6 month poststroke stages. Amusia and aphasia were behaviorally assessed at acute and 3 month poststroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA) and language tests. VLSM analyses indicated that amusia was associ- ated with a lesion area comprising the superior temporal gyrus, Heschl’s gyrus, insula, and striatum in the right hemisphere, clearly different from the lesion pattern associated with aphasia. Parametric analyses of MBEA Pitch and Rhythm scores showed extensive lesion overlap in the right striatum, as well as in the right Heschl’s gyrus and superior temporal gyrus. Lesions associated with Rhythm scores extended more superiorly and posterolaterally. VBM analysis of volume changes from the acute to the 6 month stage showed a clear decrease in gray matter volume in the right superior and middle temporal gyri in nonrecovered amusic patients compared with nona- music patients. This increased atrophy was more evident in anterior temporal areas in rhythm amusia and in posterior temporal and temporoparietalareasinpitchamusia.Overall,theresultsimplicaterighttemporalandsubcorticalregionsasthecrucialneuralsubstrate for acquired amusia and highlight the importance of different temporal lobe regions for the recovery of amusia after stroke. Key words: amusia; aphasia; music; stroke; voxel-based lesion-symptom mapping; voxel-based morphometry

Significance Statement Lesion studies are essential in uncovering the brain regions causally linked to a given behavior or skill. For music perception ability, previous lesion studies of amusia have been methodologically limited in both spatial accuracy and time domain as well as bysmallsamplesizes,providingcoarseandequivocalinformationaboutwhichbrainareasunderlieamusia.Byusinglongitudinal MRI and behavioral data from a large sample of stroke patients coupled with modern voxel-based analyses methods, we were able provide the first systematic evidence for the causal role of right temporal and striatal areas in music perception. Clinically, these results have important implications for the diagnosis and prognosis of amusia after stroke and for rehabilitation planning.

Introduction bilateral temporal, frontal, parietal, and subcortical networks in Functional and structural neuroimaging studies in healthy sub- the neural processing of music (Samson et al., 2010; Zatorre and jects have provided evidence for the large-scale involvement of Salimpoor, 2013; Koelsch, 2014). This evidence is, however,

Received March 3, 2016; revised July 10, 2016; accepted July 12, 2016. Author contributions: A.R.-F., S.S., and T.S. designed research; A.J.S., V.L., and T.S. performed research; A.J.S., P.R., V.L., and T.S. analyzed data; A.J.S., P.R., V.L., A.R.-F., S.S., and T.S. wrote the paper. Tervaniemi, Prof. Riitta Parkkola, Prof. Taina Autti, Dr. Heli Silvennoinen, Jani Saunavaara, PhD, and radiographers This work was supported by the Academy of Finland program (1257077, 1277693), Turku University Hospital Ulla Anttalainen (†), Riku Luoto, Pentti Po¨lo¨nen, and Tuija Vahtera. We also thank the patient subjects and their Research Funding, the Finnish Brain Research and Rehabilitation Foundation, the Ella and Georg Ehrnrooth Foun- families for their participation and effort. dation, the Signe and Ane Gyllenberg Foundation, the Finnish Cultural Foundation, the National Doctoral Pro- The authors declare no competing financial interests. gramme of Psychology, the Jenny and Antti Wihuri Foundation, the Formación de Profesorado Universitario Correspondence should be addressed to Dr. Aleksi Sihvonen, Cognitive Brain Research Unit, Institute of Behav- program (AP2010-4170), and the Generalitat de Catalunya (2014 SGR1413). We thank the staffs of the HUCH ioural Sciences, Siltavuorenpenger 1 B, FI-00014 University of Helsinki, Finland. E-mail: [email protected]. Department of Neurology, Turku University Hospital Department of Neurology, and other rehabilitation hospitals in DOI:10.1523/JNEUROSCI.0709-16.2016 theHospitalDistrictofSouthwestFinlandandHelsinkimetropolitanareafortheircollaboration.WethankProf.Mari Copyright © 2016 the authors 0270-6474/16/368872-10$15.00/0 Sihvonen et al. • Neural Basis of Amusia after Stroke J. Neurosci., August 24, 2016 • 36(34):8872–8881 • 8873 largely correlational, and lesion-based studies are needed to pin- Using a large (N ϭ 77) sample of stroke patients, the aim of point which brain regions are crucial and directly related to music this exploratory combined VLSM-VBM study was to determine processing. Amusia is a neural disorder characterized by severe the specific lesion patterns associated with acquired amusia and impairment of music perception and/or production caused by with changes in GM volume (GMV) and WM volume (WMV) abnormal brain development (congenital amusia) or brain dam- related to amusia recovery in a 6 month follow-up. age (acquired amusia). Deficit in perceiving fine-grained pitch changes is the hallmark symptom of amusia, but also other do- Materials and Methods mains of music, such as rhythm, can be affected (Stewart et al., Subjects and study design. Subjects (N ϭ 77) were stroke patients enrolled 2006). in two music intervention studies in Helsinki and Turku. Fifty patients Although acquired amusia is relatively common after stroke were recruited during 2004–2006 from the Department of Neurology of (ranging from 35 to 69%; Ayotte et al., 2000; Schuppert et al., the Helsinki University Central Hospital (HUCH) and 27 patients during 2000; Sa¨rka¨mo¨ et al., 2009), the exploration of its neuroanatomi- 2013–2014 from the Department of Neurology of the Turku University cal basis has been limited to symptom-led and lesion-led studies Hospital. All of the patients had an MRI-verified acute ischemic stroke or of individual cases or small (N Յ 20) patient groups. Acquired intracerebral hemorrhage in the left or right hemisphere, primarily in middle cerebral artery (MCA) territory, had cognitive or motor deficits, amusia has been associated with damage to different temporal, and were right-handed. All patients had normal hearing. Patients with frontal, parietal, and subcortical regions (Stewart et al., 2006), but prior neurological or psychiatric disease or substance abuse were not results regarding lesion lateralization (left/right) and type of mu- included. Demographic and clinical characteristics of the patients are sical deficit have been mixed, with some studies reporting spec- presented in Table 1. The individual studies were approved by the Ethics tral (e.g., pitch) or temporal (e.g., rhythm) deficits mainly after Committees of the Hospital District of Helsinki and Uusimaa and of the right hemisphere damage (Kester et al., 1991; Rosslau et al., 2015) Hospital District of Southwest Finland. Studies were performed in or after both left and right hemisphere damage (Lie´geois-Chauvel conformance with the Declaration of Helsinki. All patients signed an et al., 1998; Ayotte et al., 2000; Schuppert et al., 2000). Overall, informed consent and received standard stroke treatment and rehabili- tation. All participants underwent a behavioral assessment and an MRI constrained by small sample sizes and low spatial accuracy, these within 3 weeks of the stroke. Behavioral assessment was repeated during studies provide only coarse information about which specific the follow-up at 3 months and MRI was repeated at 6 months poststroke. brain areas are crucial for perceiving the different elements of Behavioral assessment. Music perception was evaluated by using a music. shortened version (Sa¨rka¨mo¨ et al., 2009) of the Montreal Battery of Eval- Voxel-based lesion-symptom mapping (VLSM) is a method uation of Amusia (MBEA; Peretz et al., 2003) at the acute stage (Ͻ3 weeks for analyzing the relationship between focal brain damage and after stroke) and at the 3 month poststroke stage as a part of a larger behavior by using the same voxel-based procedures used in ana- neuropsychological testing battery. We used the MBEA Scale and lyzing functional neuroimaging data (Bates et al., 2003). Com- Rhythm subtests as indices of musical pitch and rhythm perception, pared with traditional lesion analysis methods, VLSM is more respectively, and their average score as an overall index of music percep- tion (referred to hereafter as MBEA total score; Sa¨rka¨mo¨ et al., 2009). sophisticated and avoids some of the limitations of classic lesion- Following our previous study (Sa¨rka¨mo¨ et al., 2009) and the established led or symptom-led approaches (see Materials and Methods). cutoff values of the original MBEA (Peretz et al., 2003), patients with the Previously, VLSM has been used with stroke patients to deter- MBEA total score of Ͻ75% were classified as amusic [amusic, N ϭ 49; mine lesion–symptom associations related to aphasia (Geva et al., nonamusic (NA), N ϭ 28]. The amusic and NA groups were relatively 2012; Henseler et al., 2014; Fridriksson et al., 2015) as well as well matched demographically and clinically (Table 1), but the amusic attention (Grandjean et al., 2008) and motor deficits (Schoch et group had less education, larger overall lesion volume, and higher inci- al., 2006). In the music domain, VLSM has never been used be- dence of neglect. Importantly, there was no significant difference on the prestroke musical background of the patients. The Rhythm and Scale fore, whereas voxel-based morphometry (VBM), a method for ϭ analyzing gray matter (GM) and white matter (WM) differences subtest scores correlated strongly (r 0.71) at the acute stage, and there- fore only parametric analyses were carried out. between groups or across time (Ashburner and Friston, 2000), For the longitudinal VBM analyses (using only the data from the larg- has been used in studies of congenital amusia (Hyde et al., 2006, est cohort of 47 patients from Helsinki), we further subdivided the amu- 2007; Mandell et al., 2007; Albouy et al., 2013), musical training sic patients to those who showed and who did not show recovery on the (Vaquero et al., 2016), and music-based interventions in stroke MBEA from the acute to the 3 month stage. Amusic patients with Ͼ20% patients (Sa¨rka¨mo¨ et al., 2014). increase in MBEA score were classified as recovered amusics (RAs; N ϭ For congenital amusia, VBM and cortical-thickness studies 13; mean, 42%; range, 21–85%), amusic patients with a Յ20% increase have implicated structural abnormalities in superior temporal in MBEA score were classified as nonrecovered amusics (NRAs; N ϭ 16; Ϫ and inferior frontal areas as a key mechanism underlying amusia mean, 5%; range, 21 to 19%), and patients above the acute stage cutoff were classified as NAs (N ϭ 18). Overall, the three groups differed with (Hyde et al., 2006, 2007; Mandell et al., 2007; Albouy et al., 2013). respect to education, lesion volume, and neglect, but there were no dif- However, the results are mixed regarding the lateralization and ferences between the RA and NRA patients. The prestroke musical back- type (increased/decreased cortical thickness or volume) of these ground of the groups was also comparable. Across patients, changes in abnormalities, perhaps reflecting the multifaceted nature of con- the Rhythm and Scale scores from acute to 3 months did not correlate genital amusia (Omigie et al., 2012). Acquired amusia and con- significantly (r ϭ 0.25, p ϭ 0.085). genital amusia may stem at least in part from different neural To control the specificity of the lesion patterns to amusia, aphasia was bases, since the latter reflects not just impaired music perception, also assessed using the Aphasia Severity Rating Scale (ASRS) from the but a developmental deficit in acquiring musical syntax or a lack Boston Diagnostic Aphasia Examination (BDAE; Goodglass and Kaplan, of exposure to music (Stewart, 2008). To uncover the brain re- 1983). In addition, the performance of the patients in three language tests was used to derive the clinical ASRS estimate: Verbal Fluency Test [listing gions crucial for music perception, systematic and longitudinal words from a semantic category (animals); Lezak et al., 2012], shortened research on the neural basis of acquired amusia and its recovery is Token Test (De Renzi and Faglioni, 1978), and shortened Boston Nam- still needed. Clinically, this information is also important for ing test (Laine et al., 1993). establishing a more accurate diagnosis and prognosis of amusia MRI data acquisition and preprocessing. Patients from Helsinki were and for rehabilitation planning. scanned with a 1.5T Siemens Vision scanner of the HUCH Department 8874 • J. Neurosci., August 24, 2016 • 36(34):8872–8881 Sihvonen et al. • Neural Basis of Amusia after Stroke

Table 1. Demographic and clinical characteristics of the patients VLSM analysis (n ϭ 77) of Helsinki and Turku patients VBM analysis (n ϭ 47) of Helsinki patients Amusic (n ϭ 49) NA (n ϭ 28) p value NRA (n ϭ 16) RA (n ϭ 13) NA (n ϭ 18) p value Demographic Gender (male/female) 26/23 17/11 0.51 (␹2) 6/10 7/6 12/6 0.24 (␹2) Age (years) 59.9 (10.6) 55.8 (10.3) 0.09 (t) 62.2 (7.7) 58.5 (5.4) 56.6 (9.9) 0.13 (F) Education (years) 10.6 (3.7) 13.6 (3.1) Ͻ0.001 (t) 9.6 (2.6) 9.8 (4.2) 13.2 (3.1) 0.003 (F) Music background (prestroke) Formal music traininga 0.04 (0.29) 0.11 (0.58) 0.65 (U) 0.00 (0.00) 0.00 (0.00) 0.19 (0.68) 0.19 (K) Instrument playinga 1.10 (1.70) 1.96 (2.25) 0.11 (U) 1.46 (1.86) 1.26 (1.94) 1.30 (1.81) 0.90 (K) Music listening prior to strokeb 4.8 (2.0) 3.8 (1.8) 0.96 (U) 3.3 (1.8) 3.4 (1.4) 3.9 (1.1) 0.61 (K) Clinical Aphasia (no/yes)c 29/20 17/11 0.89 (␹2) 10/6 10/3 11/7 0.61 (␹2) BDAE-ASRS 4.3 (1.1) 4.4 (0.9) 0.51 (U) 4.3 (1.1) 4.5 (1.1) 4.3 (1.1) 0.89 (K) MBEA total score percentage 58.0 (8.6) 84.3 (6.2) Ͻ0.001 (t) 57.8 (10.5) 55.2 (8.8) 84.9 (7.1) Ͻ0.001 (F) MBEA Rhythm score percentage 58.1 (12.0) 82.0 (8.9) Ͻ0.001 (t) 60.3 (13.8) 56.0 (12.3) 84.1 (9.7) Ͻ0.001 (F) MBEA Scale score percentage 57.8 (12.3) 86.6 (8.4) Ͻ0.001 (t) 55.5 (12.6) 54.4 (8.5) 85.7 (9.2) Ͻ0.001 (F) Visual neglect (no/yes)d 26/18 28/0 Ͻ0.001 (␹2) 9/7 7/6 18/0 0.004 (␹2) Other cognitive dysfunction (no/yes)e 2/47 4/24 0.11 (␹2) 0/16 1/12 2/16 0.41 (␹2) Hemiparesis (no/yes) 11/38 8/20 0.55 (␹2) 4/12 2/11 4/14 0.81 (␹2) Lesion laterality (left/right) 18/31 16/12 0.08 (␹2) 8/8 3/10 10/8 0.17 (␹2) Lesion volume in cm3 55.8 (43.7) 25.5 (27.8) 0.001 (t) 57.6 (46.4) 49.2 (35.9) 25.3 (31.7) 0.04 (F) Data are mean (SD) unless otherwise stated. ␹2, ␹2 Test; F, one-way ANOVA; K, Kruskal–Wallis test; t, t test; U, Mann–Whitney U test. aNumbers denote values on a Likert scale: 0, no; 1, Ͻ1 year; 2, 1–3 years; 3, 4–6 years; 4, 7–10 years; 5, Ͼ10 years of training/playing. bNumbers denote values on a Likert scale: range, 0 (never does) to 5 (does daily). cClassification based on BDAE ASRS: scores 0–4, aphasia; score 5, no aphasia. dClassification based on the Lateralized Inattention Index of the Balloons Test. eOther cognitive dysfunction (attention/executive function or memory deficit). of Radiology to obtain high-resolution T1 images (flip angle, 15°; TR ϭ more sophisticated than traditional lesion analysis, where either a lesion- 1900 ms; TE ϭ 3.68 ms; voxel size, 1.0 ϫ 1.0 ϫ 1.0 mm). Patients from led or a symptom-led approach has been used. In lesion-led studies, Turku were scanned using a 3T Siemens Verio scanner of the Medical patients are grouped on the basis of lesions to a particular brain region Imaging Centre of Southwest Finland. The structural MRI was a T1- and then tested for behavioral differences on a given domain compared weighted MPRAGE image (flip angle, 9°; TR ϭ 2300 ms; TE ϭ 2.98 ms; with a control group (Chao and Knight, 1998). Although this provides voxel size, 1.0 ϫ 1.0 ϫ 1.0 mm). information about the functional roles of the lesioned brain areas, the Normalization of MRI images to a standard template is a necessity for spatial resolution of this method is coarse and often overlooks lesion precise comparisons between different subjects or groups, which is espe- areas outside the specific region of interest (areas outside the specific cially important when dealing with abnormal brain tissue, such as in injury site are not considered affecting the behavioral performance and stroke patients. To achieve optimal normalization with no postregistra- valuable information may be lost). In symptom-led studies, the brain tion lesion shrinkage or out-of-brain distortion, cost function masking region contributing to the cognitive deficit is being deduced from lesion- (CFM) was applied (Brett et al., 2001). To define the cost function masks, overlap images of patients sharing the same behavioral deficit (Dronkers, A.J.S. and T.S. created binary masks of the lesioned areas by manually 1996). To classify patients, this method requires a specific cutoff value on depicting, on a slice-by-slice basis, the precise boundaries of the lesion a behavioral task, and parametric analysis is not possible. Thus informa- directly into the T1 image (Ripolle´s et al., 2012). An overlap image of all tion of varying performance across a broad spectrum outside cutoff val- patients’ lesions and a statistical power map are shown in Figure 1. The ues is lost. As distinct from the previous, VLSM does not require patient MRIcron software package (http://people.cas.sc.edu/rorden/mricron/ grouping by lesion site or behavioral data cut-off, although allows binary index.html, RRID:SCR_002403) was used to do the aforementioned le- variables as well. In three-dimensional lesion maps, formed from MRI sion tracing (Rorden and Brett, 2000). images, each voxel is analyzed and the presence, or absence, of a lesion is T1 images and binary lesion masks were then processed using the correlated with the behavioral data. Finally, statistical results and maps of Statistical Parametric Mapping software (SPM8, Wellcome Depart- areas associated with the behavioral deficits are shown. ment of Cognitive Neurology, University College London, RRID: VLSM was performed with nonparametric mapping software (Chris SCR_007037) under Matlab 8.0.0 (MathWorks; version R2012b, RRID: Rorden’s NPM, version 6, June 2013) using the normalized acute phase SCR_001622). Unified segmentation (Ashburner and Friston, 2005) lesion maps for all patients. Parametric analyses were performed using with medium regularization and CFM was applied to T1 images, seg- the MBEA total (Scale and Rhythm average) score, Rhythm score, Scale menting them precisely into GM, WM, and CSF probability maps before score, and the BDAE-ASRS score. Also, the following binary analyses normalizing them into the Montreal Neurological Institute (MNI) space. were performed: amusic versus NA; amusic (no aphasia) versus aphasic This technique has been widely used with stroke patients (Crinion et al., (no amusia); amusic versus NA (all aphasics excluded); aphasic versus 2007; Andersen et al., 2010; Ripolle´s et al., 2012). To preserve the original nonaphasic (all amusics excluded); and amusic versus amusic and apha- signal strength during the normalization, GM and WM segmented im- sic. All voxels damaged Ն10% of the patients were included in the statis- ages were modulated. Smoothing of the tissue probability maps was per- tical analysis (Dovern et al., 2011; Mirman et al., 2015; Timpert et al., formed by using an isotropic spatial filter (FWHM, 6 mm) to reduce 2015). Correction for multiple comparisons was achieved by using a false residual interindividual variability. In addition, the lesion masks defined discovery rate (FDR)-corrected p Ͻ 0.05 threshold. in native space were also registered to MNI space using the normalization Voxel-based morphometry. Morphometric analysis was performed us- parameters obtained during the segmentation process. ing SPM8. Individual smoothed GM and WM images were entered into Voxel-based lesion symptom mapping. VLSM enables analysis of the a second-level analysis using a Group (RA/NRA/NA) ϫ Time (Acute/6 relationship between local brain tissue damage and behavioral data in a months) mixed between-subject and within-subject ANOVA. Six differ- voxel-by-voxel basis (Bates et al., 2003). By providing a detailed func- ent Group (RA Ͼ NRA, RA Ͼ NA, NRA Ͼ NA) ϫ Time (Acute Ͼ6 tional map of lesioned brain regions affecting in a given task, VLSM is months, 6 months Ͼ Acute) contrasts were calculated. Unless otherwise Sihvonen et al. • Neural Basis of Amusia after Stroke J. Neurosci., August 24, 2016 • 36(34):8872–8881 • 8875

Figure 1. Lesion-overlap maps for all subjects. A, Lesion distribution for the whole sample. Warmer areas indicate areas of greater lesion overlap. Accordingly, the color scale ranges from 2 to 26 overlapping subjects. B, Statistical power map for VLSM analyses with 77 patients. The map is thresholded at Z Ͼ 4 to view lesion areas with sufficient statistical power to detect an effect of Z Ն 4. noted, results were thresholded at a whole-brain uncorrected p Ͻ 0.001 patients. Correspondingly, we excluded amusic patients and threshold at the voxel level and a FWE-corrected p Ͻ 0.05 at the cluster compared pure aphasic versus nonaphasic patients. This yielded Ͼ level with a cluster extent of 50 contiguous voxels. Anatomical and essentially the same results as the first binary analysis, i.e., lesions cytoarchitectonical areas were identified using the Automated Anatom- in the right STG, MTG, HG, putamen, and insula were linked to ical Labeling Atlas (Tzourio-Mazoyer et al., 2002) included in the xjView amusia (Fig. 3A), and lesions in the left HG and insula were linked toolbox (http://www.alivelearn.net/xjview8/, RRID:SCR_008642). As VBM uses voxel intensities and different acquisition settings in different to aphasia (Fig. 3B). Similarly, when amusic patients were com- MRI scanners can have a great effect on the results, we used only patients pared with patients with both amusia and aphasia, lesions in the from the Helsinki study with acute and 6 month images (N ϭ 47). As right STG, MTG, putamen, and insula were again significant (Fig. stated earlier, patients were from a larger music intervention study 3C). We additionally compared purely amusic (no aphasia) to (Sa¨rka¨mo¨ et al., 2008), but importantly the intervention and control purely aphasic (no amusia) patients, and lesions associated with groups of this previous study did not differ in the recovery from amusia amusia comprised yet again the right STG, putamen, and insula, as indexed by change in the MBEA average score (Sa¨rka¨mo¨ et al., 2009). and in aphasia the lesion pattern was localized in the left HG and Additionally, the number of NA and amusic patients in the music inter- insula (Fig. 3D). vention group was approximately the same (Sa¨rka¨mo¨ et al., 2009). Therefore, the results were not biased in any way by the intervention To determine whether the lesion patterns were similar for received. pitch and rhythm perception, we performed separate VLSM analyses for the MBEA Scale and Rhythm scores. Parametric Results analyses (Fig. 4A,B) indicated a largely overlapping pattern of Lesion patterns associated with amusia and aphasia lesions in the right STG, MTG, HG, insula, and basal ganglia To identify lesion areas associated with amusia and aphasia, we (BG; putamen, caudate, pallidum) for low scores on both Scale first performed parametric VLSM analyses for the MBEA total and Rhythm subtests (Fig. 4C). Differences in amount of vox- scores and BDAE-ASRS scores across all patients. Lower MBEA els damaged in separate anatomical areas are presented in Ta- total scores were associated with lesions in the right superior ble 2. The same anatomical areas were seen in both Scale and temporal gyrus (STG), middle temporal gyrus (MTG), Heschl’s Rhythm, but the lesions associated with low Rhythm scores gyrus (HG), putamen, and insula (Fig. 2A). In contrast, lower covered a larger proportion of the areas. When the lesion BDAE-ASRS scores were associated with lesions in the left STG pattern associated with poor performance in the Rhythm sub- and insula (Fig. 2B). We then performed binary VLSM analyses test was further analyzed by limiting the results only to the directly comparing amusic versus NA patients and aphasic versus most significant areas (t value Ͼ4.5), the right putamen and nonaphasic patients. As in the parametric analyses, amusia was caudate remained as the most significant components. associated with lesions in the right STG, HG, putamen, and insula (Fig. 2C), and aphasia was associated with lesions in the left STG and insula (Fig. 2D). GM and WM changes associated with amusia recovery To control for the comorbidity of amusia and aphasia (41% of The NRAs had greater GMV decrease in the right STG and amusics had concurrent aphasia, 65% of aphasics had concurrent MTG than the NAs, as shown by a Time (Acute Ͼ6 months) ϫ amusia), we performed the same binary analyses first excluding Group (NRA Ͼ NA) interaction (Fig. 5A). No significant aphasic patients and then comparing pure amusic versus NA changes in WM were found in the aforementioned interaction. 8876 • J. Neurosci., August 24, 2016 • 36(34):8872–8881 Sihvonen et al. • Neural Basis of Amusia after Stroke

Figure 2. VLSM results for amusia and aphasia. A, MBEA Scale and Rhythm average score (%). B, BDAE-ASRS score. C, Amusic versus NA patients. D, Aphasic versus nonaphasic patients. Amusia is based on MBEA Scale and Rhythm average score (cutoff, 75%) and aphasia is based on BDAE-ASRS (cutoff, Ͻ5). N ϭ 77. Neurological convention is used with MNI coordinates at the bottom left of each slice. All statistical maps are thresholded at a FDR-corrected p Ͻ 0.05 threshold. INS, Insula; PUT, putamen.

Figure 3. Binary lesion analyses with pure amusics and pure aphasics. A–D, Comparisons between A, amusic versus NA patients (all aphasic patients excluded; N ϭ 47); B, aphasic versus nonaphasicpatients(allamusicpatientsexcluded;Nϭ28);C,amusicversusaphasicandamusicpatients(Nϭ49);andD,amusic(noaphasia)versusaphasic(noamusia;Nϭ40).Amusiaisbased on MBEA Scale and Rhythm average score (cutoff, 75%) and aphasia is based on BDAE-ASRS (cutoff, Ͻ5). Neurological convention is used with MNI coordinates at the bottom left of each slice. All statistical maps are thresholded at a FDR-corrected p Ͻ 0.05 threshold. INS, Insula; PUT, putamen.

Other contrasts (NRA Ͼ RA; RA Ͼ NA) did not yield any acquired amusia after stroke, with additional temporal GMV and significant results. WMV decrease observed in patients with persistent amusia. To our When the two MBEA subtests (Rhythm and Scale) were ana- best knowledge, this is the first large-scale VLSM-VBM study to lyzed separately, we found that in the Rhythm subtest the NRAs systematically explore the neural basis of acquired amusia and its showed more GMV decrease in anterior temporal areas than the recovery. Importantly, both lesion studies and neuroimaging studies NAs (Fig. 5B). In this comparison, there was also greater WMV of healthy subjects are needed to better understand the neural sys- decrease in inferior temporal areas in the NRAs than the NAs tems underlying a given cognitive task (Price and Friston, 2002; (Fig. 5B). In the Scale subtest, the NRAs showed greater GMV Rorden and Karnath, 2004), and our results provide more direct decrease in the posterior temporal/temporoparietal area than the evidence for the role of the aforementioned regions in music percep- NAs [interaction: Time (Acute Ͼ6 months) ϫ Group (NRA, tion. Moreover, compared with results from congenital amusics, Scale Ͼ NA, Scale; p Ͻ 0.01 uncorrected; Fig. 5C]. All significant who are born with a dysfunctional music perception system or are GMV and WMV decreases are shown in Table 3. not able to acquire musical syntax during early development, leading Discussion to a life-long musical deficit, results from acquired amusia can reveal The main finding of the present study was the distinct pattern of information about the neuroanatomy of a normally developed and lesions in right temporal, insular, and striatal areas underlying previously functioning music perception system. Sihvonen et al. • Neural Basis of Amusia after Stroke J. Neurosci., August 24, 2016 • 36(34):8872–8881 • 8877

Figure 4. VLSM results for scale and rhythm amusia. A, MBEA Scale score. B, MBEA Rhythm score. C, An overlap image comparing Rhythm (red) and Scale (blue) subtests’ parametric results (Nϭ77).NeurologicalconventionisusedwithMNIcoordinatesatthebottomleftofeachslice.AllstatisticalmapsarethresholdedataFDR-correctedpϽ0.05threshold.GP,Globuspallidum;PUT, putamen.

Table 2. Anatomical correlates of VLSM results for MBEA Scale and Rhythm scores disorders by performing binary analysis on amusics (no aphasia) Anatomical region Scale Rhythm versus aphasics (no amusia), amusics versus NAs with all aphasics Right caudate 90.02% 94.86% excluded, aphasics versus nonaphasics with all amusics excluded, Right HG** 40.58% 90.53% and amusics versus amusics and aphasics. Importantly, com- Right insula* 38.56% 59.23% pared with the primary analyses (Fig. 2), these secondary analyses Right pallidum 86.82% 89.81% (Fig. 3) yielded essentially the same key areas, thereby providing Right putamen 90.02% 94.86% further support for the dissociation of the neuroanatomical cor- Right MTG** 1.97% 18.55% relates of amusia and aphasia. This finding is important given the Right STG** 11.25% 57.04% longstanding discrepancy between the results of lesion studies Right middle temporal pole 0.70% 1.98% Right superior temporal pole 2.70% 6.33% and functional neuroimaging studies of healthy subjects, the for- mer reporting cases of selective impairment of music and lan- The lesion patterns associated with poor performance in MBEA Scale and Rhythm subtests were overlaid with anatomical masks created with the WFU PickAtlas to obtain the amount of damaged voxels in different anatomical guage (Sidtis and Volpe, 1988; Piccirilli et al., 2000; Mendez, regions. For visual results, see Figure 4. 2001; for review, see Peretz and Coltheart, 2003) and the latter *p Ͻ 0.05, **p Ͻ 0.005; comparison (␹2) between the percentages of damaged voxels in Rhythm versus Scale often reporting significant anatomical overlap in responses to (i.e., if the damaged area within the structure is larger in Rhythm than in Scale). music and speech (Maess et al., 2001; Abrams et al., 2011; Rogal- sky et al., 2011; for a recent meta-analysis, see LaCroix et al., Lesions associated with amusia and lower MBEA total scores 2015). Although our results point to a clear lateralization of amu- were all located in the right MCA territory whereas the lesions sia and aphasia, music processing is a largely bilateral process, associated with aphasia and lower BDAE-ASRS scores were lo- and studies with larger samples of LHD patients are still needed to cated in the left MCA territory. This is in line with previous group determine how different left hemisphere regions overlap or sep- and case studies that have linked the right MCA territory (Ayotte arate in amusia and aphasia. et al., 2000; Rosslau et al., 2015) and especially the right temporal Separate VLSM analyses of the parametric MBEA Scale and lobe lesions (Mazzoni et al., 1993; Kohlmetz et al., 2003; Terao et Rhythm scores revealed a largely overlapping lesion pattern in the al., 2006; Sa¨rka¨mo¨ et al., 2010; Hochman and Abrams, 2014)to aforementioned right hemisphere network. Naturally, stroke le- acquired amusia. Previous VLSM studies on aphasia have also sions follow the vasculature of the brain and thus are not re- indicated lesions in the left STG and insula (Geva et al., 2012; stricted to a single functional brain area. To pursue the most Henseler et al., 2014; Fridriksson et al., 2015). In the present crucial areas in VLSM results, the t-value cutoff can be adjusted to sample, 41% of the amusic patients had at least minor aphasia, only view the most significant areas in that certain lesion pattern. which is similar to the percentages (43–55%) reported in previ- When this approach was applied to the lesion pattern associated ous amusia case and group studies (Schuppert et al., 2000; with poor performance in the Rhythm subtest, the right BG re- Stewart et al., 2006) and suggests that musical and language def- mained the most significant element. To our best knowledge, this icits can often occur together. Although the present sample size is the first lesion study to directly implicate the right BG in prevented us from performing a full 2 ϫ 2 analysis [amusia (no/ rhythm perception deficits. In previous studies of healthy sub- yes) ϫ aphasia (no/yes)] or separate analyses within left hemi- jects, BGs have been linked closely to rhythm analysis (Penhune sphere damage (LHD) and right hemisphere damage (RHD) et al., 1998; Grahn and Brett, 2009; Grahn and Rowe, 2009; Alluri subgroups, we were able to control for the comorbidity of these et al., 2012). Recently, professional pianists were also shown to 8878 • J. Neurosci., August 24, 2016 • 36(34):8872–8881 Sihvonen et al. • Neural Basis of Amusia after Stroke

Figure5. VBMresultsof47Helsinkipatients.Time(AcuteϾ6months)ϫGroup(NRAsϾNAs)inGMVorWMVinteraction.A,GMNRAs(Nϭ16)versusNAs(Nϭ18).B,GM(hot)andWM(cold) nonrecoveredrhythmamusics(Nϭ11)versusnonrhythmamusics(Nϭ18).C,GMnonrecoveredpitchamusics(Nϭ13)versusnonpitchamusics(Nϭ17).BarplotsforGMVdifferencesinAcute Ͼ6months.Bar,mean;errorbar,SEM.TheresultsarereportedatanuncorrectedpϽ0.001thresholdatthevoxellevel(extentthreshold:kϾ50voxels)usingMNIcoordinates.Clustersforanalysis A and B pass a p Ͻ 0.05 FWE-corrected threshold at the cluster level.

Table 3. GMV and WMV decreases (6 month–acute) have skill-related plastic GMV changes in the putamen (Vaquero Anatomical area MNI coordinates Cluster size t value et al., 2016). Another key area associated with rhythm processing is the cerebellum (Penhune et al., 1998; Sakai et al., 1999); how- GM NRAs versus NAs Right MTG (BA 21) 54 Ϫ33 Ϫ3 8443 4.20* ever, due to our inclusion criteria, cerebellar involvement in the Right STG (BA 22) 53 Ϫ35 Ϫ4 rhythm amusia could not be evaluated. Right inferior temporal gyrus 58 Ϫ62 Ϫ6 In line with previous studies, we found lesions in the right supe- (BA 37) rior temporal cortex to be associated with pitch amusia (Lie´geois- GM and WM nonrecovered rhythm Chauvel et al., 1998; Ayotte et al., 2000; Terao et al., 2006; Hochman amusics versus nonrhythm and Abrams, 2014). Converging evidence from neuroimaging stud- amusics ies of healthy subjects indicates that right superior temporal areas are Right STG (BA 38) 47 17 Ϫ41 15866 4.85* crucial for pitch and melodic processing (Griffiths et al., 1998; Guts- Ϫ Ϫ Right middle temporal pole 53 17 32; 65 0 9 chalk et al., 2002; Patterson et al., 2002; Tramo et al., 2002; Hyde et Ϫ Ϫ Right MTG WM 50 24 14 3931 4.66* al., 2008) and that their volume correlates with musical practice (Sei- Right IFG WM 49 Ϫ8 Ϫ26; 49 Ϫ17 Ϫ18 GM nonrecovered pitch amusics ther-Preisler et al., 2014) and absolute pitch (Wengenroth et al., versus nonpitch amusics 2014). In all of our analyses, right insular lesions were also signifi- Right MTG (BA 21) 63 Ϫ62 6 790 3.76** cantly associated with amusia. Lesions in the insula have previously Right MTG (BA 19) 61 Ϫ69 12; 63 Ϫ64 Ϫ6 been linked to pitch amusia after stroke (Terao et al., 2006; Hoch- For visual VBM results, see Figure 5. IFG, Inferior Temporal Gyrus. man and Abrams, 2014), but may also represent damage to WM *p Ͻ 0.05 FWE-corrected at the cluster level. fibers in the right frontotemporal pathway, the connectivity of which **p Ͻ 0.01 uncorrected at the cluster level has been found to be decreased in congenital amusics (Loui et al., 2009; Albouy et al., 2013). Sihvonen et al. • Neural Basis of Amusia after Stroke J. Neurosci., August 24, 2016 • 36(34):8872–8881 • 8879

In the longitudinal VBM analysis, persistent amusia was In conclusion, the results of the present study show that linked to increasing GMV decline in the right STG, where damage to the right temporal areas, insula, and putamen structural abnormalities have also been observed in congenital forms the crucial neural substrate for acquired amusia after amusics (Hyde et al., 2007; Albouy et al., 2013). GM anomalies stroke. Persistent amusia is associated with further atrophy in in congenital amusics are associated with decreased connec- the right STG and MTG, locating more anteriorly for rhythm tivity between the right STG and the right frontotemporal amusia and more posteriorly for pitch amusia. It is possible areas (Hyde et al., 2006; Albouy et al., 2013). Correspondingly, that lesions associated with acquired amusia damage the right we observed a WMV decrease in a region of this same pathway frontotemporal pathway, explaining the WMV decrease, pos- in the NRA patients. Interestingly, further GM atrophy was sibly leading to neuronal degeneration and consequent GMV present in anterior temporal areas in rhythm amusics and decrease. Overall, compared with older lesion studies, these posterior temporal areas in pitch amusics. Similar within- findings provide a more detailed and fine-grained view of the hemisphere functional segregation with the anterior STG and neuroanatomy of amusia and, conversely, highlight which ar- superior temporal sulcus showing more sensitivity to changes eas are crucial and necessary for normal music perception. In in the temporal domain and posterior regions showing more clinical stroke practice, these results have important implica- sensitivity to changes in the spectral domain has been reported tions for more accurate identification of amusia, better pre- both in animals (Bendor and Wang, 2008) and in humans diction of the outcome of amusia recovery, and more precise (Warren et al., 2005; Jamison et al., 2006; for a meta-analysis, targeting of music-based rehabilitation methods. see Samson et al., 2010). Also in temporal lobectomy patients, anterior temporal lesions have been linked to deficits in the perception of temporal cues (meter, tempo; Kester et al., 1991; References Abrams DA, Bhatara A, Ryali S, Balaban E, Levitin DJ, Menon V (2011) Lie´geois-Chauvel et al., 1998). Decoding temporal structure in music and speech relies on shared brain Another feature of spectral versus temporal processing ob- resources but elicits different fine-scale spatial patterns. 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